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After determining each topic negativity value, the input text negativity value can be determined by extracting the topic of input text and get the negativity value from the topic. These algorithms were chosen because they have shown good accuracy in many text classification task Pang, Bo. We employ this feature based on our observation that among sarcasm text, there are 20 text with interjection words. If the text is using interjection words, the text has more tendency to be classified into sarcasm text. There are a quite a lot of text with personal message that can only be analy. After that, the opinion text will be classified into positive or negative class.

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We employ this feature based on our observation that among sarcasm text, there are 20 text with interjection words. Based on our observation of the sarcasm data, we added the features of negativity and number of interjection words.

Taman Titiwangsa Hulu Langat.

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In the translation, one Indonesian word with more than one English translation is given the average score of all the English translations. The first classification is to classify each text into three sentiment classes: The cute cat from the guest house holder home.

This reminds me exactly of that. I remember everyone used to draw the same scenery when we were kids, Sun rising between a mountain and a river flowing to a small hut. The unigram taken from the text is only the term that exists in our translated SentiWordNet.

Additional features also showed that Indonesian people tend to write their critics using cemungudu. Both of the classification use the sentiment score feature as one of the base feature. We also employed translated SentiWordNet in the sentiment classification.

The negativity feature tried to catch the global sentiment value, while the interjection feature represents the lexical phenomena of the text message.

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Construct a stochastic model that accurately represents the behaviour of the process. Maps every feature set into a two dimensional plane and construct a model that based on a linear line that separate the class from the mapped feature set.

Mughal Garden, Rashtrapati Bhawan gate no cemunguxh After that, the opinion text will be classified into positive or negative class. As for the low accuracy, we found that there are many sarcasm texts have no global topic. The last male northern white rhino died today.

Evaluated the additional features of negativity and interjection number in the sarcasm. Rather than directly classify a text into three classes, at first, this method classify a text into opinion and neutral text. Nature at its best! Yeah, I didn't had the river and hut there.

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The second classification is to classify the sarcasm of the possitive text. Often used in automated text classification because it is simple and did not need a lot of data compared to another machine learning algorithm [8]. We compared two things in the experiment: There are two classification steps. If the text is using interjection words, the text has more tendency to be classified into sarcasm text.

After determining each topic negativity value, the input text negativity value can be determined by extracting the topic of input text and get the negativity value from the topic.

Grand Perron Des Encombre m D'altitude. Represents the percentage of the negative sentiment in the topic of the text message.

We only classify positive sarcasm text is because almost all of the sarcasm text is looked like positive text, while the real value of the text is negative. These algorithms were chosen because they have shown good accuracy in many text classification task Pang, Bo.

We proposed two additional features to detect sarcasm after a common sentiment analysis is conducted. To get this feature, the topic of the text message should be extracted first.